This article will cover what a beginner should know on how to start using AI in your business. It will also discuss the benefits of doing so and how you can get started.

What Is The Advantage Of Using AI In Your Business?

So you may be wondering how AI can improve customer experience. Well, AI can help you identify issues that customers experience. It can detect them and then recommend solutions.

For example, you can use AI to record customer feedback on their purchasing experience. Or, you can use AI to automate certain customer service tasks, such as responding to frequently asked questions. It will take charge of those tasks that can be easily automated, eliminating senseless expenses and helping you gain some time.

Ways In Which You Can Implement AI In Your Business

If you want to start using AI technology in your business, you should consider a few factors. We will go over some of them below:

1.  Understand What AI Can And Cannot Do

AI can help you scale certain processes and make better decisions by providing more accurate data. However, there are certain things AI cannot do. For example, AI cannot ascertain a customer’s emotional state as well as a human can. Therefore, AI might not be your best solution if your business requires a lot of empathy or human interaction.

You also need to consider the cost of implementing AI into your business. If you have the budget, AI can greatly assist your company. But if you do not, it may weigh against your gains. It is known that deciding whether or not to implement AI into your business is a tough decision. As a result, you must weigh the pros and cons and decide what’s best for your company.

2.  Consider Why You Need the AI

As a business owner or manager, it’s important to consider your aims. Why do you want to implement AI into your business? What are you hoping to achieve? How will AI help you reach your goals?

You can use AI in many different ways in your business. One approach is to use AI to automate tasks currently being done manually. It can help improve efficiency and free up employees’ time to focus on more strategic tasks.

Another approach is to use AI to supplement or replace human workers in certain roles. It can be particularly beneficial in repetitive roles or those requiring high accuracy, such as data entry.

No matter your approach, it’s important to ensure you implement AI in a way that aligns with your business goals.

3.  Constantly Test The AI

It’s important to test the AI. It is required to do so even for a period after implementation. All these precautions aim to ensure that it functions as intended. Then, you can monitor the AI’s performance and compare it to expected outcomes. If the AI is not performing as intended, you can make adjustments to improve its performance.

It’s important to clearly understand how the AI is intended to function before you begin the testing. It will help ensure that the results of the test are accurate. You should conduct the tests in a controlled environment to identify and address any unexpected results. After the test is complete, it’s important to analyze the results and ensure that the AI functions properly.

The Bottom Line

As you can see, you can use AI technology in many ways in your business. By following the tips in this guide, you can get started with AI and begin reaping the benefits it has to offer. So what are you waiting for? Start using AI today and take your business to the next level!

Also Read: Artificial Intelligence And Machine Learning In Controlling Are In Advance

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Before explaining these two technologies, it is necessary to remember the definition of their origin: Artificial Intelligence. This resides in the capacity of a machine to process the data it captures, which in turn are the result of previous experiences. This processing is similar to the functioning of the human brain, which captures information and transforms it to generate knowledge.

What Are Machine Learning And Deep Learning?

First of all, machine learning, as a derivative of AI, involves the creation of algorithms that can modify themselves without human presence.

Machine learning is a data analysis method based on the premise that our systems learn from that data. Through such a method, plans will be able to identify patterns and make decisions without any intervention on our part.

Deep Learning

On the other hand, deep learning is a type of machine learning whose function is to train a system to learn by itself. This competition is possible by recognising patterns and executing tasks such as those that we human beings do. As a relevant fact, this branch of AI uses a specific class of algorithms called neural networks.

Although the algorithms are created and work similarly to machine learning, multiple layers of neural networks are responsible for individually providing a different interpretation of the data. These networks have the purpose of trying to imitate the function of the neural networks of our brain. This includes voice recognition, object detection, and image identification. Voice assistants such as Amazon’s Alexa or Apple’s Siri are based on this technology. Even biometric recognition systems for fingerprints, face, voice, etc., also have this type of technology.

Machine Learning And Deep Learning Have Different Capabilities.

A deep learning model is designed to continuously perform data analysis while maintaining a logical structure similar to a human being. To achieve this type of analysis, deep learning must use layers of algorithmic systems, those mentioned above, artificial neural networks. These networks are the ones that allow much more advanced knowledge than the basic machine learning models. Deep learning facilitates the automation of training processes and is capable of creating its criteria automatically, altogether dispensing with human intervention.

In short, machine learning and deep learning are almost the same, as they work in the same way but have different capabilities. Although the basic models of machine learning are continually evolving, their functions still require monitoring on our part. Suppose an artificial intelligence algorithm gives an incorrect prediction. As a result, we will have to intervene and apply the necessary adjustments. By having this model, an algorithm will be able to determine if a prediction is incorrect by utilizing its neural network.

Advantages Of Machine Learning and Deep Learning

Many organizations have basic or advanced Artificial Intelligence applications, and their use continues to spread.

Regardless of the productive sector or the size of these companies, implementing this technology helps solve common day-to-day problems to the most complex. For this reason, this technology has a very positive impact on efficiency and profitability.

In particular, companies that manage large amounts of data must rely on machine learning and deep learning; since these resources can be used in different areas, from finance and health to marketing and sales.

We can summarize part of the advantages of AI in these areas:

• Speed ​​in the management and processing of data and identification of relevant information.
• Ability to analyze consumer behavior with greater precision.
• Fraud detection and prevention, specifically in the banking and insurance sector.

Even machine learning and deep learning support making the right decisions in companies. Likewise, they increase the capacity for efficient and intelligent work, reducing the percentage of human error and adding competitive advantages.

How Can Machine Learning And Deep Learning Be Helpful in Our Company?

Machine learning and deep learning contribute decisively to our company obtaining scalability, more excellent performance, and cost and time savings. In addition, these technologies can also provide the following benefits:

• They personalized customer service. It allows analyzing user preferences so that personalized products can be offered automatically. In this way, the perception that customers have of our company is improved, thus enhancing loyalty. For example, platforms such as Netflix, YouTube and Spotify constantly use this technology to suggest other content based on what we have enjoyed. 
• Process automation. Of course, one of the most relevant contributions of the two technologies that we analyze is the automation of routine tasks. The latter absorbs a lot of time and effort from human talent and does not provide added value. Using machine learning, our systems can detect the processes they must deal with.
• Reduce errors. The automatic learning of the management systems applied in the organization means that the mistakes made are not repeated. The longer it stays in the system, the more resilient it will be.
• Preventive actions. Based on the above, machine learning tools can prevent bugs and errors. Artificial Intelligence can exclude any action that compromises or puts the development of products or services at risk.

Other Important Uses

• Cybersecurity. Undoubtedly, the contribution of this technology to the protection of networks, systems and terminals of organizations against risks of cyberattacks is significant. It should be noted that most malware uses similar code, so the use of machine learning can prevent their meddling.
• Fraud detection. Thanks to the technologies we are dealing with, it is possible to detect which transactions are legitimate and which are not accessible. It is even feasible to reveal the mismanagement of resources. Such a function is achievable when a pattern is assigned to financial movements.
• Medical diagnoses. When implemented in the technological tools of the health system, these technologies help insurers be more intuitive about possible health problems, depending on the frequency of medical consultations. Apart from that, these technologies offer more reasonable costs and recommend different medication options, among others.
• Improves the security and integrity of information. Cloud storage is another service that facilitates these two strands of AI.

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Chatbots as digital service employees

Chatbots are emerging as a serious competitor for apps right now. They are independent of operating systems such as iOS or Android and can be operated via their conversational interface by voice or text. They can be used on the most popular messaging channels such as Facebook Messenger, WhatsApp, Telegram and web messaging. If these tools are used in social networks, they are called social bots.

Social bots act as profile users and like, comment and share posts. However, companies can also use them as a communication tool with customers. With the digital helpers, you simulate real communication partners. This means that it is pretended that there is a real person or a real employee behind the profile. Renowned studies predict that in 2020, 85% of all customer interactions will be controlled by artificial intelligence and bots and that no human interaction will be required for this. (Gartner, 2017).

Chatbots can be created on websites. The programs can be selected according to the intended use or created yourself. The real effectiveness of the digital communication partner can only be seen in practical use. Numerous bots can influence the opinion-forming of consumers. For example, Oxford University found that during the first television debate between Donald Trump and Hillary Clinton, more than every third pro-Trump tweet was posted by a digital helper. Large and well-known companies have already started to develop chatbots and see the integration of a bot as an absolute necessity for the digital evolution of companies.

Chatbots As Insurance Advisors

The insurance group ARAG uses chatbots. Mobile customer advice is provided via Facebook Messenger with the help of a chatbot. This enables users to obtain information on insurance benefits. The following example is about travel assistance advice.

By using the messenger and the emojis available there, conversations with the chatbot get a relaxed atmosphere. The chatbot allows the company to react to inquiries in the shortest possible time and present the appropriate services.

Advantages and Disadvantages of Chatbots

The digital communication partners offer companies numerous advantages:

• Automatic interaction: The interaction with consumers can be carried out automatically via messaging platforms.
• Resource conservation: Chatbots can be used as personal assistants and automated live agents, especially in ​​customer service, and can save companies up to 80 per cent of support costs.
• Easy integration: Chatbots can be easily integrated into the infrastructure.
• Shorter distances: From the customer’s point of view, there is faster access to information and simplified interaction with the company, which is possible around the clock.

But there are also disadvantages:

• Manipulation: The use of chatbots enables public opinion to be manipulated, e.g. US election campaigns.
• Communication with a program: The use of programs can be perceived as impersonal from the customer’s point of view.
• Programming: To be accepted by customers, a chatbot must be programmed for error-free communication. He must not just answer questions but must be modelled on natural language dialogues.
• Data control: The transfer of customer data to chatbots must be compatible with data protection.

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This article aims to give you an overview of machine learning paradigms and the types of problems they are commonly used for.

Machine Learning Paradigms

As a general rule (there are exceptions), machine learning algorithms build a model representing the knowledge they have been able to extract from the data provided as input. Depending on the additional information supplied to the algorithm, we can differentiate between different paradigms to guide the learning process. Below I briefly describe the best known:

• Supervised learning. It consists of indicating to the algorithm, as it learns if the output it has generated for a particular case (the prediction) is correct or not. The most common action is for the algorithm to adjust the model it generates each time it is told that it has made a mistake to improve its predictions.
• Unsupervised learning. The only information that is delivered to the algorithm is the data samples without further details. From these samples, it is possible to analyze the distribution of the values, the similarity or distance between the models, the degree of concurrence of some variables with others, etc. The applications are multiple, as we will see later.
• Semi-supervised learning. It is a case halfway between the previous two. From the available data set, the correct output is known only for some samples. The algorithm uses them to build an initial model that, later, provides a forecast of the output value for the rest of the pieces. In this way, the model is expanded and adjusted, taking advantage of the available information.
• Reinforcement learning. The algorithm to which the data is provided is not supplied with the accurate outputs to adjust its model, as is the case in the supervised point. Still, it is awarded a more or less significant prize depending on how well the sequence of actions is carried out. In this way, the behaviour is reinforced towards the objective pursued.

These paradigms allow specific types of problems to be solved and implemented using different tools: the models that represent knowledge. Depending on the chosen model: a tree, a neural network, a set of rules, etc., a specific algorithm will be used to generate and fit it.

Types of Problems in Machine Learning

Machine learning is used to solve a wide range of real-life problems. These problems, or tasks as they are also known, can be categorized into a few types. Although it is not a strict rule, each situation is usually addressed through a specific learning paradigm. For this reason, the most common types of tasks are outlined below according to the paradigm with which it is traditionally approached.

Supervised learning Tasks

There are two fundamental types of problems that are solved by supervised learning, described below. The actual outputs, known in advance for the data, will allow the algorithm to improve its model parameters. Once the teaching or training of the model is completed, it will be able to process new samples and generate the appropriate output without any help.

• Classification. Each data sample has associated one or more nominal outputs, called class labels, labels, or simply class. To automatically classify, a predictive model is created, to which, by delivering the input variables, it generates the corresponding class labels as output. A classifier can be used to process credit or risky loan applications, differentiate incoming email messages as spam or essential, find out whether or not a person’s face appears in a photograph, etc.
• Regression. As in the previous case, each sample also has an associated output value, but in this case, it is of an objective type (continuous, not discrete, that is, with possible results within a continuum), so the techniques used to generate the model are usually different from those used for classification. However, the procedure for fitting or training the model is similar: known accurate outputs are used to correct its parameters and improve prediction. With a regression model, it is possible to determine the height of a person based on their sex, age and nationality, or to predict the distance that will be able to travel a transport taking as input variables the weight of the load, the volume of fuel available and the ambient temperature.

Also Read: What Are Artificial Intelligence (AI), Machine Learning (ML), And Deep Learning (DL)?

Unsupervised Learning Tasks

As indicated above, the types of problems faced with this learning paradigm are characterized because the data samples only have the input variables. There is no way out to predict that can guide algorithms. Therefore, the models generated, if they exist, are not predictive but descriptive. The most common tasks are:

• Grouping. Analyzing the similarity/dissimilarity of the data samples, for example, calculating the distance they are from each other in the space generated by the values ​​of their variables. Several disjoint groups are created. This technique, also known as clustering, facilitates visual data exploration and can be used as a primary classification method when the required class labels are not available to generate a classifier.
• Association. The search for associations between specific values ​​of the variables that make up the samples is carried out by looking for the concurrence between them, that is, by counting the times they appear simultaneously. As a result, this type of problem can generate a set of association rules, a technique widely used in all kinds of electronic and physical businesses to arrange their products or recommend them.
• Variable reduction. By analyzing the distribution of the values ​​of the variables in the set of samples, it is possible to determine which of them provide more information, which is correlated with others and therefore are redundant, or whether it is possible to find an underlying statistical distribution that generates these data, which would simplify its original representation. There are many possible techniques in this type of task, from the selection and extraction of variables to manifold learning, consisting of finding the aforementioned underlying distribution.

Other Types of Learning Tasks

A vast majority of the problems addressed through machine learning fall into the categories listed in the previous two sections. However, there are other types of tasks that require different approaches. An example would be optimization problems in general, of which perhaps the best-known exponent is the travelling salesman. This task consists of finding the shortest itinerary to visit in cities. When n is enormous, the problem becomes unapproachable to the exhaustive search: evaluating all the possible alternatives to determine the best one.

There are many other cases within this category, and the difficulty is usually always the same: the optimal point is not known, so it cannot be known whether a potential solution is more or less good, and the number of possible solutions, or steps to reach them, it is enormous. There are two categories of techniques that are commonly applied to deal with these problems:

• Bio-inspired algorithms. This group includes genetic algorithms, evolutionary strategies, optimization based on particle systems, etc. All of them start from the same concept: reproduce mechanisms existing in nature such as evolutionary selection in living beings, the behaviour of flocks of birds, colonies of ants, etc. Thanks to them, it is possible to find an acceptable solution to the optimization problem in a reasonable period.
• Reinforcement learning. This paradigm, described at the beginning of the section, can also be applied to optimization problems, although in recent times, it has gained notoriety for its success in learning to play and win certain games.

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What is AI?

Artificial Intelligence is a branch of a computer system that works and acts the way humans do. Eg: SIRI, Alexa, facial recognition, self-driving cars, which “pop” up for us when we think of AI.

When AI Takes Over

Artificial Intelligence is replacing high-level, judgment-based skill sets. Many repetitive tasks will be replaced by robots in the future. This leaves people doubting about future jobs.

Why should you re-skill and retrain?

New technologies can unleash their potential in a short time. Hence, private organizations and government sectors need to create concrete plans to re-engineer the workforce and identify effective ways to ensure a smooth transition for their employees in the short term when automation has the most effect. Some of the reasons to re-skill are as follows:

1. Key to win AI talent war.
2. Save organizations from spending a lot of money on hiring the right talent.
3. Can lead to higher performance by employees.
4. It will help to reintegrate into the workplace.

Strategies to Reskill Your Employees

When looking to integrate new technologies like AI in your product, it is important to ensure that you are not replacing the employees, rather upskill them with necessary courses, tools.

Here are three skills to ensure your collaboration with employees and AI.

1. Once you Identify the skills to work on, Anticipate the new types of skills that are needed to do that job, and hence, ask your employees to upskill with some of the artificial intelligence courses available across the internet so that your employees are ready when the time comes.

1. Laying off your old employees and hiring new ones is a costly process. Instead, keep your current employees, as they are familiar with your company’s product, growth, customers.

1. Your business needs to refine your employee’s skills. Training, development, and deployment can take a long time, however, take time and work with your employees to enhance your company’s product.

3 Ways To Reskill Your Workforce

To drive toward organizational goals, employees must adapt to the workplace changes.

What Is Reskilling?

Reskilling is all about developing a new skill. Often reskilling allows employees to take on a new role within an organization.

Reskilling: If your company has business analysts, data analysts or data engineering, then they could be good candidates to train for AI tasks. This includes focusing on skills like Python, R, NLP, and TensorFlow, which is a deep learning framework. Many educators like Great Learning, Coursera, and Udemy provide Artificial intelligence certifications online for upskilling. This not only helps employees to develop AIML skills but also gets value for the company with existing experience in AI to be a leader and mentor for developing employees.
Here are 3 ways to effectively reskill your workforce:

1. Evaluate And Strategize

Evaluating your learning strategy starts with an honest assessment.

• What is your current learning strategy that helps your organization?
• Have you been hitting these goals?
• Do you have any existing skills gaps?

After assessing your current state of learning, you need to determine whether these goals are still relevant.

• Are different behaviors or skills required for your people to perform their jobs effectively?
• Which skills will drive your business forward?
• Check if your sales and marketing team has transitioned from in-person to virtual sales?
• Check if you are still on in-shopping or home delivery?

These types of changes are necessary for learning strategy.

2. Get Started And Be Agile

Once your new learning strategy is ready, it’s time to get started! Implement, test, and then improve based on what you’ve learned. In doing so, you’ll quickly identify skills that are necessary for the growth of your business.
However, reskilling can not only help your organization to face a new challenge but also close current critical talent gaps but will better prepare you to master future disruptions as well.

3. Moving Forward

Newmarket crises constantly force us to evaluate our learning programs. COVID-19 has sought to drive its organization forward in reskilling. We must set sound strategies, test our learnings, and maintain our investment if we want to find success.

Organizations are re-skilling their workforce.

Leading organizations are creating programs to retrain their employees. One way to upskill workers is to allow individuals to learn new skills from different tools. During COVID, many of the learning platforms came forward to give out their resources for free to help professionals enhance their skills, and Great Learning is an excellent example. These platforms offer free training for both professionals and fresh aspirants to upskill themselves. Also, organizations can take advantage of these platforms and ask their employees to reskill.

Why is Continuous Learning Via Online Training Programs Crucial?

According to experts, AIML needs continuous learning!
One needs to learn languages such as Python, R, and the fundamentals of Statistics and Machine Learning concepts. Then, to take it to the next level, one needs to do enhancement with advanced concepts of Machine learning algorithms, NLP, neural networks, etc. All of this requires extensive training from top online educations, but most companies may not provide it.

Yet, some organizations like Wipro are working to ensure that skills gaps with employees and run multiple initiatives aimed at reskilling employees.

Here I would like to add up few courses that might help your employees to re-skill

1. Post Graduate Certification In Artificial Intelligence and Machine Learning- By Great Learning in collaboration with Texas McCombs.
2. Applied Machine Learning Course- By Applied AI Course.
3. Post Graduate Course in Machine Learning and AI – By Amity Online.
4. Artificial Intelligence Course For Leaders- By Great Learning in collaboration with Texas McCombs.
5. Columbia University’s Artificial Intelligence Course – By Pearson Professional Programs.
6. Artificial Intelligence – By Kellogg School of Management.
7. Machine Learning: Fundamentals and Algorithms – By Carnegie Mellon University.
8. Machine Learning AI Certification by Stanford University- By Coursera.
9. The Business of AI- By London Business School.
10. 10.  Learn AI from ML experts at Google – By Google

Summary

Undoubtedly, AI is a huge asset to all processes and is going to disrupt many industries and economies. Hence, it is costly to look and hire new AI experts. Instead, make strategies to help reskill your employees for a better understanding of the technology. No change comes without results, but I am certain that we will find solutions to reskill employees with AI and other emerging technologies. With new learning, we can decrease this disruption and create a better future of work.

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Chatbots answer customer questions, intelligent robotics control system maintenance, and voice recognition simplify the handling of mobile devices: AI is already used in a variety of ways. In IT operations, the new technologies can be found under the keyword Self-Driving Network. The advantage of the self-controlling network: The IT environment requires significantly less manual intervention. It also becomes more reliable and safer.

Collect and evaluate data in a targeted manner

The first thing to do is to know what is going on in the network. This requires meaningful data on devices and systems as well as on network and service performance – ideally in real-time. In some cases, monitoring solutions still determine this information via SNMP (Simple Network Management Protocol). However, with this method, the network load is very high and the recorded data is sometimes inaccurate. Accurate real-time performance monitoring can only be achieved with streaming telemetry. Because streaming telemetry is push-based. It continuously transmits reliable data according to defined guidelines from different sources such as routers, switches, or firewalls to a central platform.

AI already helps to speed up analyzes here – because administrators can ask their questions directly in natural language. “What’s wrong with my switch?”, “What was the performance of the WLAN network last Friday?” Or “How do my switch uplinks work?” Is an answer to an AI-supported solution in detail. It also provides help for troubleshooting. Furthermore, AI technologies support predictive analyzes. If anomalies occur, proactively notify the administrator. All of this happens before a user even realizes that there is a network problem.

Driving automation forward

Another prerequisite for a self-controlling network is extensive automation. Modern control software already offers extensive options for this, for example, to flexibly direct data flows and avoid bottlenecks. In the WAN, for example, this is already possible today with solutions for software-defined WAN (SD-WAN). Its cornerstones are intelligent data routing, zero-touch provisioning, and unified threat management. Administrators first prioritize the data traffic and the applications. The solution then routes the data intelligently in day-to-day operations by automatically and dynamically selecting the most suitable WAN transmission path. Zero-touch provisioning ensures that new devices such as switches or routers can be automatically put into operation at distributed locations. Unified Threat Management, in turn, acts as a protective shield over the entire network infrastructure. The focus of the administrators is shifting from routine activities to more demanding tasks: defining the set of rules and handling exceptional cases.

Also Read: The IT security Trends For 2021

IT learns to walk

In phase 3, the self-regulating network, the administrator hardly intervenes at all. An AI-supported network management solution learns from the collected and evaluated data. The implemented guidelines and automation then channel the implementation. If all these components are connected, the system learns adaptively and can control and optimize itself to a certain extent.

In this way, the system identifies the causes of problems in LAN or WLAN, among other things, and automatically initiates measures. An intelligent, self-learning solution such as the AI-supported Mist Systems platform from Juniper Networks, for example, adds missing VLAN configurations, corrects an incorrectly configured switch port, or adjusts the radio resource management of the access points required. If an AI engine cannot yet resolve an anomaly, such as a change in the bandwidth pattern or changed server behavior, it proactively notifies the administrator and learns from troubleshooting.

That brings it

Today’s network has to be fast, agile, and fail-safe. Companies can counter the increasing administrative effort with autonomous networks. These will increasingly set up, control, analyze and optimize themselves. It makes you safer and more reliable – because networks identify and fix potential performance problems before users even notice. But experience shows: The implementation of an autonomous network takes time and a sure instinct. Every organization has individual needs for which the right solution and strategy must be determined.

The post The Artificial Intelligence In Three phases To Autonomous IT appeared first on TechReviewsCorner.

In the past, where smart technologies could be adapted, there was too often a lack of profitability, a sparkling business idea or customer acceptance. The view of the insurance industry is therefore characterized by subdued euphoria for 2021 when it comes to the notable breakthrough of technologies. However, leaps in development are to be expected. Some technologies will be used more and more methodically by insurers. Other technologies will at least influence the industry and its business and bring about one or the other novelty.

Robotic Process Automation (RPA), Artificial Intelligence (AI), Cloud Computing

Dark processing, both in application processing and claims processing, has the greatest chance of making a mighty leap in 2021. The advantage of RPA over other technology approaches: Insurers do not have to make any drastic interventions in their ongoing processes. Small successes can be achieved quickly without having to set up large IT projects. This brings relief, but no breakthrough in operational efficiency.

In this regard, much greater effects can be achieved with the digitization of application routes and the fully automated cancellation processing. Automated underwriting also has the chance to become more widespread in 2021. Insurers will increasingly rely on standard covers in order to be able to implement end-to-end automation without traditional underwriting. In the case of major risks or tenders, AI solutions are so advanced that they support the process and significantly shorten it.

Internet of Things (IoT)

The Internet of Things, especially the industrial one, has the greatest potential for real market innovations. For example, the IoT is creating new business models such as pay-per-use. These rental models of machine capacity create new risks that have to be insured, such as failure risks. In 2021, more industrial insurers will also calculate usage-based policies – similar to telematics tariffs

Electromobility

The number of e-cars will increase significantly in 2021. This means that insurers will change their car tariffs. It will be more complicated to clarify how components interact and which manufacturer or supplier is liable for a defect or control error. In addition, defective batteries often lead to total write-offs. Insurers, together with manufacturers and authorities, will deal with these challenges more intensively in 2021 than this year. In addition, insurers will jump on the e-car train and start with special safeguards, for example the inclusion of certain components in the insurance cover or the assumption of the disposal costs for the battery.

Naturale Language Generation (NLG)

Even in reporting processes, AI is on the verge of relieving more and more monotonous work. Insurance companies are facing more and deeper tests by BaFin and other authorities. The trigger for this is, for example, the Wirecard affair. Insurers must also provide information on solvency and financial position (SFCR) on a regular basis in a page-long report. The data are unstructured in the insurers’ IT systems. Currently, an average of five employees take care of turning this data into written reports. With the help of NLG, an AI sub-area, natural language text can be automatically generated in very good quality – faster and more efficiently than humans would be able to do. In addition, the technology is suitable

5G, fiber optics, autonomous driving

World is facing a boom in investments in digital infrastructure and technologies. These projects are extremely interesting for insurers because they promise reasonably good and, above all, sustainable returns in times of low interest rates. In 2021, these technologies will thus indirectly gain relevance, namely as investment objects.

Chatbots

When using chatbots, insurers go through a maturation process. We’re going to see some small developments. However, completely bot-based communication via the website, as with Lemonade, will hardly establish itself as a standard. However, the lockdown as a result of the corona pandemic has certainly worked like a magnifying glass that changes in communication are necessary and possible. It can be assumed that by 2021 more insurers will not only use chatbots for written communication, but also take more of the workload off their call centers. The real added value will arise when customers complete their concerns together with the bot, i.e. when a chatbot also automatically changes the address, creates the contribution information or provides information on the status of a claim.

Technologies will primarily help with the renovation in 2021

One thing is clear: by the end of 2021, insurers will be more digitized than they are now. The progress will probably also be more noticeable than in the previous year. The current corona pandemic has played a major role in this. However, real disruptive effects are not to be expected. Above all, technologies will help improve processes, optimize IT and thus ensure cost efficiency. This competitive advantage will become significantly more relevant in all industries. In addition, technologies will help insurers to evaluate larger amounts of data in real time and thus support them to take existing business to a new level. Real technology-driven market innovations in the form of revolutionary policies and business models will be seen sporadically in 2021.

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Artificial intelligence is nothing new

Maybe you’ll be disappointed…
But artificial intelligence originated in the 1950s by the American computer scientist and researcher John McCarthy. Who then described artificial intelligence as a machine that can think just like a human. Since then, the development has continued with some “notches” in the curve for interest in the area.

But in the mid-90s, for example, Deep Blue became the first chess-playing computer system (IBM) to beat world chess champion, Garry Kasparov.

Why is so much happening around AI now?

One factor that affects the increased interest in artificial intelligence is that today there is great access to large amounts of data. It facilitates the creation of various AI solutions. Other factors that affect interest and development are:

– Cheap cloud services make it possible to create AI solutions at a reasonable cost.

–  Today there are frameworks and tools that simplify the work of developing AI solutions.

–  Computer power in the form of fast processors adapted to AI.

–  A lot of interest in AI breeds even more interest in AI and in developing new AI solutions.

Areas of application for artificial intelligence

Nothing is new under the sun, it’s usually hot. What makes AI more difficult today is that there are differences from “before”.

OCR (optical character recognition), ie text interpretation, is no longer perceived as an example of “artificial intelligence”, as it has become a routine technology for a long time.

The difference today compared to before is that current OCR applications integrated with AI technology, provide enormously improved accuracy and speed because it uses machine learning technology.

You can divide the area of ​​artificial Intelligence into different areas of application. A few examples are:
Reasoning functions
In this area, there are various solutions for data analysis (data science) for forecasting and probability-based solutions.

An example is being able to predict how many items of a certain type need to be purchased for a company or warehouse. Where the data is based on historical information or real-time information from various sources.

Examples of how AI can be used

AI and machine learning are around you today in real-time.

Just take, for example, Facebook’s face recognition, the best way suggestions in Google Maps, or the personalized recommendations on Amazon (more on these later in the text).

CRM with AI

An important parameter to follow up for all companies is customer loss (Customer Churn Rate). This is due to the fact that it is cheaper to retain current customers than to acquire new ones. Loss of customers is simply a lost value for the company. In this area, AI can be used to predict which customers are considering leaving the company so that they can be contacted.

Do you want more areas where AI is used in CRM? Take a look at the video below about Salesforce Einstein, an AI tool that helps companies get a data-driven sales culture.

Many manufacturing companies already collect large amounts of data from various plant sensors during its production. Information that is a perfect basis for AI. Where the information can be used for fault detection and quality control without human intervention.

Another area in production where AI is used is planning and schedule optimization. By being able to quickly predict when different machines are available, it leads to more efficient and optimized manufacturing. In the video below you can see how BMW uses AI to handle deviations in real-time. 

AI in service and aftermarket

AI is an excellent tool for preventive maintenance, needs planning, and aftermarket activities. That is, to continuously check when parts in a machine or engine must be replaced, even before something has broken.

AI in e-commerce

AI can be used to individually customize e-commerce and web pages. Using algorithms, AI can predict what each customer and visitor wants and display the most relevant products and recommendations automatically. 

We are also changing the way we consume content on, for example, an e-commerce site. Today we search by writing. But more and more people are being introduced to search using pictures, videos, or speech.

AI to analyze Big Data 

With analysis of large amounts, for example, traffic data from websites, e-mail, or other network data.

All to find deviations from the normal that involve some form of security threat. Where the changes take place so quickly and the amounts of data are so enormous from both own and other sources that it is difficult to draw the right conclusions. There is AI logic that gives you information about what changes you should act on.

AI in economics and finance 

Few areas are better suited for AI and machine learning than the economics area. This is in view of the fact that these are often large volumes of data. Today, AI and algorithms are used in stock trading, lending, and insurance to assess risks. But also in follow-up and analysis. 

Benefits of AI

Decision

AI and machine learning algorithms can prioritize and automate your decisions. They can also alert you to immediate action. AI can also process both historical data and input data in real-time. Which means you can react to what’s happening right now.

Analysis and insight

AI can analyze large, complex amounts of data and from these reach its own insights in a way and at a speed that is beyond our human ability.

Efficiency

With AI, the company’s efficiency can be significantly improved in, for example, scheduling and planning, automation of tasks, or quality control.

The post What Can Artificial Intelligence Do In Your Business? appeared first on TechReviewsCorner.

Without a doubt, the most cutting-edge technological trend of recent years is everything related to artificial intelligence and data analysis. And it is that relatively recently there have been great advances in this field, which together with the availability of enormous amounts of data and increasing computing power are giving rise to all kinds of very interesting practical applications.

The problem comes when the terms related to the field become marketing empty words that in many cases are outright lies. It is very common to talk that this or that product uses artificial intelligence to achieve something and, sometimes, they are conventional algorithms making predictable decisions.

What is Artificial Intelligence?

Artificial intelligence (AI) was born as a science many years ago when the possibilities of computers were really limited, and it refers to making machines simulate the functions of the human brain.

AI is classified into two categories based on its capabilities:

• General (or strong) AI: that tries to achieve machines/software capable of having intelligence in the broadest sense of the word, in activities that involve understanding, thinking, and reasoning on general issues, on things that any human being can do.
• Narrow (or weak) AI: which focuses on providing intelligence to a machine/software within a very specific and closed area or for a very specific task.

Thus, for example, a strong AI would be able to learn by itself and without external intervention to play any board game that we “put before it”, while a weak AI would learn to play a specific game like chess or chess. Go. What’s more, a hypothetical strong AI would understand what the game is, what the objective is, and how to play it, while the weak AI, although it plays Go better than anyone else (a tremendously complicated game), will not really have a clue what it is doing.

One of the crucial questions when it comes to distinguishing an artificial intelligence system from mere traditional software (complex as it may be, which brings us to the jokes above) is that AI “programs” itself. That is, it does not consist of a series of predictable logical sequences, but rather they have the ability to generate logical reasoning, learning, and self-correction on their own.

The field has come a long way in these years and we have weak AIs capable of doing incredible things. Strong AIs remain a researcher’s dream and the basis of the scripts for many science fiction novels and films.

What is Machine Learning?

Machine Learning (ML) or machine learning is considered a subset of artificial intelligence. This is one of the ways we have to make machines learn and “think” like humans. As its name suggests, ML techniques are used when we want machines to learn from the information we provide them. It is analogous to how human babies learn: based on observation, trial, and error. They are provided with enough data so that they can learn a certain and limited task (remember: weak AI), and then they are able to apply that knowledge to new data, correcting themselves and learning more over time.

There are many ways to teach a machine to “learn”: supervised, unsupervised, semi-supervised, and reinforcement learning techniques, depending on whether the correct solution is given to the algorithm while it is learning, it is not given the solution, it is Sometimes you give or are only scored based on how well or poorly you do, respectively. And there are many algorithms that can be used for different types of problems: prediction, classification, regression, etc …

You may have heard of algorithms such as simple or polynomial linear regression, support vector machines, decision trees, Random Forest, K nearest neighbors … These are just some of the common algorithms used in ML. But there are many more.

But knowing these algorithms and what they are for (to train the model) is just one of the things that need to be known. Before it is also very important to learn how to obtain and load the data, do an exploratory analysis of the same, clean the information … The quality of the learning depends on the quality of the data, or as they say in ML: “Garbage enters, garbage comes out”.

Today, the Machine Learning libraries for Python and R have evolved a lot, so even a developer with no knowledge of mathematics or statistics beyond that of the institute, can build, train, test, deploy and use ML models for applications of the real world. Although it is very important to know all the processes well and understand how all these algorithms work to make good decisions when selecting the most appropriate for each problem.

What is Deep Learning?

Within Machine Learning there is a branch called Deep Learning (DL) that has a different approach when creating machine learning. Their techniques are based on the use of what are called artificial neural networks. The “deep” refers to the fact that current techniques are capable of creating networks of many neural layers deep, achieving unthinkable results a little more than a decade ago, since great advances have been made since 2010, together with large improvements in computing power.

In recent years Deep Learning has been applied with overwhelming success to activities related to speech recognition, language processing, computer vision, machine translation, content filtering, medical image analysis, bioinformatics, drug design … obtaining results equal to or better than those of human experts in the field of application. Although you don’t have to go to such specialized things to see it in action: from Netflix recommendations to your interactions with your voice assistant (Alexa, Siri, or Google assistant) to mobile applications that change your face … They all use Deep Learning to function.

In general, it is often said (take it with a grain of salt) that if the information you have is relatively little and the number of variables that come into play is relatively small, general ML techniques are best suited to solve the problem. But if you have huge amounts of data to train the network and there are thousands of variables involved, then Deep Learning is the way to go. Now, you must bear in mind that the DL is more difficult to implement, it takes more time to train the models and it needs much more computing power (they usually “pull” GPUs, graphics processors optimized for this task), but the problems are usually more complex as well.

What is Big Data?

The concept of Big data is much easier to understand. In simple words, this discipline groups the techniques necessary to capture, store, homogenize, transfer, consult, visualize, and analyze data on a large scale and in a systematic way.

Think, for example, of the data from thousands of sensors in a country’s electrical network that send data every second to be analyzed, or the information generated by a social network such as Facebook or Twitter with hundreds (or thousands) of millions of users. We are talking about huge and continuous volumes that are not suitable for use with traditional data processing systems, such as SQL databases or SPSS-style statistics packages.

Big Data is traditionally characterized by 3 V:

• The high volume of information. For example, Facebook has 2 billion users and Twitter about 400 million, who are constantly providing information to these social networks in very high volumes, and it is necessary to store and manage it.
• Speed: following the example of social networks, every day Facebook collects around 1 billion photos and Twitter manages more than 500 million tweets, not counting likes and many other data. Big Data deals with that speed data receiving and processing so that it can flow and be processed properly without bottlenecks.
• Variety: the infinity of different types of data can be received, some structured (such as a sensor reading, or alike ) and others unstructured (such as an image, the content of a tweet, or a voice recording). Big Data techniques must deal with all of them, manage, classify, and homogenize them.

Another of the great challenges associated with the collection of this type of massive information has to do with the privacy and security of said information, as well as the quality of the data to avoid biases of all kinds.

As you can see, the techniques and knowledge necessary to do Big Data have nothing to do with those required for AI, ML, or DL, although the term is often used very lightly.

These data can feed the algorithms used in the previous techniques, that is, they can be the source of information from which specialized models of Machine Learning or Deep Learning are fed. But they can also be used in other ways, which leads us to …

What is Data Science?

When we talk about data science, we refer in many cases to the extraction of relevant information from data sets, also called KDD ( Knowledge Discovery in Databases, knowledge discovery in databases). It uses various techniques from many fields: mathematics, programming, statistical modeling, data visualization, pattern recognition, and learning, uncertainty modeling, data storage, and cloud computing.

Data science can also refer, more broadly,  to the methods, processes, and systems that involve data processing for this extraction of knowledge. It can include statistical techniques and data analysis to intelligent models that learn “by themselves” (unsupervised), which would also be part of Machine Learning. In fact, this term can be confused with data mining  (more fashionable a few years ago) or with Machine Learning itself.

Data science experts (often called data scientists ) focus on solving problems involving complex data, looking for patterns in the information, relevant correlations, and ultimately, gaining insight from the data. They are usually experts in math, statistics, and programming (although they don’t have to be experts in all three).

Unlike experts in Artificial Intelligence (or Machine Learning or Deep Learning ), who seek to generalize the solution to problems through machine learning, data scientists generate particular and specific knowledge from the data from which they start. Which is a substantial difference in approach, and in the knowledge and techniques required for each specialization.

The post Clarifying The Concepts Of Various Technology Terms – Artificial Intelligence, Deep Learning, Machine Learning, Big Data, and Data Science appeared first on TechReviewsCorner.

Currently, the development of artificial intelligence (AI) as an autonomous discipline is undergoing a decisive transition towards these goals. A sufficient level is already shown to bring concrete solutions to the general public and to fill functions previously reserved for the brightest minds.

It has also reached a point of maturity that allows a practical deployment of autonomous specialties, including deep learning.

The Foundations Of Deep Learning

Artificial intelligence as a scientific discipline serves multiple objectives that divide the field of knowledge into more or less autonomous areas. Recognition of three-dimensional objects has little to do with the problems posed by machine translation. But in both cases, algorithms derived from years of work in this scientific field are used.

From the beginnings of artificial intelligence, two fundamental orientations appeared in this field. One aimed to address the material and logical bases of consciousness. That is a complete and mechanical simulation of rational human thought. The other orientation sought to address specific problems to give them in each case a solution derived from automated data processing.

It goes without saying that the second orientation is the one with the most real technology applications at work. The creation of robots that somehow mimic the human mind is still a long way off. But it is another thing to tackle problems where reality can be broken down into numerically treatable data.

Machine learning imitates a set of tests within a parameterizable tightly closed system. With it, he looks for correspondences and relationships that allow predicting a future result. Work with decision trees, apply inductive logic programming, and any effective technique to read, classify and categorize large masses of data.

In these techniques, there is an economy of computing resources. It seeks to maximize the results with a minimum of process load and execution time. The algorithms that are part of the body of knowledge of these specialties aim for a system to find by itself correspondence generating new relationships or dependencies.

Deep learning is a subfield of more recent development, it has a theoretical consistency since 2010. The specificity of this orientation is not to guide systems with complex systems algorithms. It starts with simpler models that are applied to a real case to imitate its operation.

In the case of a complex execution board game like chess in the deep learning application, no instructions are given. It seeks to produce advantages in the game that approximate a victory. To do this, the rules of the game are followed, and by the brute force of trials, the system creates its own rules or criteria to search for the master moves. The good results in this and other practical cases make it to be tested in a multitude of problems presented in real life.

The usual way to produce decision models by patterns found within the system itself is called a neural network system. The name itself suggests the source of inspiration used as the basic scheme: the nervous system. However, this meeting point has its own implementation for each problem that arises.

Also Read: Artificial Intelligence (AI) In Marketing And Sales

Utilities And Main Uses Of Deep Learning

A very promising example of deep learning was that experienced by Google with AlphaZero. It is a large-scale computer system in terms of hardware that aims to teach the machine to play chess in hours at the highest level.

The capacities acquired by the system with this technology were later subjected to a confrontation with a conventional program in its conception but of great performance.

The tests carried out at the end of 2017 have been more than positive, the capabilities acquired by deep learning brilliantly surpassed those given by large teams of programmers to the aforementioned commercial program.

The areas of science where this specialty of artificial intelligence is most interesting are the following :

• In molecular biology the structural analysis of proteins.
• Asset and portfolio management in financial markets.
• Studies of fluid mechanics in aeronautical engineering.
• Discovery of pathological patterns in images taken by magnetic resonance systems.
• Climatic and historical climatology studies.
• Production of new materials and nanotechnology.
• Solving conjectures and mathematical problems.
• Security tests with cryptography algorithms.
• Cosmology and models of the structure of matter.
• Genetic research.

Current difficulties in the application of deep learning

The most productive techniques in artificial intelligence require high data processing capacity. The tests are usually run on machines tailored to the experiment you want to carry out. This leaves the possibilities of achieving goals with some economic and social impact in the hands almost exclusively of large technology companies.

However, the scientific culture acquires new tools to meet its most immediate objectives. It is also undeniable that the results end up being part of social life with special services and products at some stage of their conception or production.

The confluence of disciplines such as big data makes it easy to divide the current high requirements for classic problems into intermediate steps. The frameworks like Hadoop and Spark facilitate the handling of large volumes of information in various media.

Collaboration between different technologies is of the utmost importance when scientists move on the frontiers of science. The expected fruits will mark the coming years with a new scientific revolution that will make information the main raw material in the world.

The goal of reaching human perception

The partial successes with deep learning have reopened the theoretical possibility of simulating the form of human understanding in machines. The degree of difficulty of even this still distant goal is completely unknown. But it is a measure of the level reached today.

An important milestone is a feat carried out in 2012 by the team led by Andrew Ng by distinguishing a cat in a set of ten million video files. Skills like this will be available to millions of systems spread across every country in the world.

The security and control of social networks against users with unethical behavior goes through a review of the information that reaches them. The construction of intelligent systems would make it easier to get rid of the most negative effects on these instruments for citizen participation.

Image tagging can only be done automatically by recognizing the figures that are presented in them. The same for the written information contained in files with image settings. These operations are essential to detect trolls and put coercive measures on them to prevent their actions.

The direct applications of artificial intelligence technologies are mainly built for new needs. Singularly with born by the intervention of technology in society. But we are only at the beginning of a great change. Scientific production and the energy for new advances will have the invaluable support of these technologies in a cycle that has no end in sight.

Also Read: MACHINE LEARNING FOR COMPANIES: ADVANTAGES OF ARTIFICIAL INTELLIGENCE FOR YOUR BUSINESS

Conclusion

Artificial intelligence is in a mature moment that gives it a great role as a direct factor of production. The social and economic changes that will ensue are difficult to foresee. In this dynamic, deep learning is one of the most fruitful orientations. In just over eight years he has shown a great ability to attack unapproachable problems with other means.

A current difficulty that is difficult to tackle for artificial intelligence is machine translation. The last step with which machines could unambiguously understand human written or verbal commands. The complexity of new neural networks with the participation of some new technique may allow machines to communicate with human language.

Human perception is the necessary prelude to generate thought in symbolic language. Transferring this human capacity to a machine is not easy, nor is it known if it can be done indistinguishable. But when deep learning models try to scale a nervous system they only use a basic model.

Artificial intelligence is called to be present in all aspects of human life. From education to the conquest of space, any dimension of reality has an appointment with this technological instrument. It is advisable to be attentive to the news that will accompany the latest innovations in this field. It justifies that they have enough entity to completely transform the world we know.

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